Efficient verification of machine learning applications

ABSTRACT

An example operation may include one or more of generating, by a training participant client comprising a training dataset, a plurality of transaction proposals that each correspond to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, a batch from the private dataset, a loss function, and an original model parameter, receiving, by one or more endorser nodes of peers of a blockchain network, the plurality of transaction proposals, and evaluating each transaction proposal.

TECHNICAL FIELD

This application generally relates to consensus processes for machinelearning applications, and more particularly, to efficient databasemachine learning verification.

BACKGROUND

A centralized database stores and maintains data in a single database(e.g., a database server) at one location. This location is often acentral computer, for example, a desktop central processing unit (CPU),a server CPU, or a mainframe computer. Information stored on acentralized database is typically accessible from multiple differentpoints. Multiple users or client workstations can work simultaneously onthe centralized database, for example, based on a client/serverconfiguration. A centralized database is easy to manage, maintain, andcontrol, especially for purposes of security because of its singlelocation. Within a centralized database, data redundancy is minimized asa single storing place of all data also implies that a given set of dataonly has one primary record.

However, a centralized database suffers from significant drawbacks. Forexample, a centralized database has a single point of failure. Inparticular, if there are no fault-tolerance considerations and ahardware failure occurs (for example a hardware, firmware, and/or asoftware failure), all data within the database is lost and work of allusers is interrupted. In addition, centralized databases are highlydependent on network connectivity. As a result, the slower theconnection, the amount of time needed for each database access isincreased. Another drawback is the occurrence of bottlenecks when acentralized database experiences high traffic due to a single location.Furthermore, a centralized database provides limited access to databecause only one copy of the data is maintained by the database. As aresult, multiple devices cannot access the same piece of data at thesame time without creating significant problems or risk overwritingstored data. Furthermore, because a database storage system has minimalto no data redundancy, data that is unexpectedly lost is very difficultto retrieve other than through manual operation from back-up storage. Assuch, what is needed is a solution that overcomes these drawbacks andlimitations.

SUMMARY

One example embodiment provides a system that includes a trainingparticipant client, comprising a training dataset and configured toperform one or more of generate a plurality of transaction proposalsthat each correspond to a training iteration for machine learning modeltraining related to stochastic gradient descent, the machine learningmodel training comprising a plurality of training iterations, thetransaction proposals comprising a gradient calculation performed by thetraining participant client, a batch from the private dataset, a lossfunction, and an original model parameter, and a blockchain network,comprising one or more endorser nodes or peers, each comprising a verifygradient smart contract configured to perform one or more of receive theplurality of transaction proposals, and evaluate each transactionproposal.

Another example embodiment provides a method that includes one or moreof generating, by a training participant client comprising a trainingdataset, a plurality of transaction proposals that each correspond to atraining iteration for machine learning model training related tostochastic gradient descent, the machine learning model trainingcomprising a plurality of training iterations, the transaction proposalscomprising a gradient calculation performed by the training participantclient, a batch from the private dataset, a loss function, and anoriginal model parameter, receiving, by one or more endorser nodes ofpeers of a blockchain network, the plurality of transaction proposals,and evaluating each transaction proposal.

A further example embodiment provides a non-transitory computer readablemedium comprising instructions, that when read by a processor, cause theprocessor to perform one or more of generating, by a trainingparticipant client comprising a training dataset, a plurality oftransaction proposals that each correspond to a training iteration formachine learning model training related to stochastic gradient descent,the machine learning model training comprising a plurality of trainingiterations, the transaction proposals comprising a gradient calculationperformed by the training participant client, a batch from the privatedataset, a loss function, and an original model parameter, receiving, byone or more endorser nodes of peers of a blockchain network, theplurality of transaction proposals, and evaluating each transactionproposal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a block diagram of verify gradient transactionendorsement in a blockchain, according to example embodiments.

FIG. 1B illustrates a block diagram of a training participant network,according to example embodiments.

FIG. 1C illustrates a block diagram of a training participant network,according to example embodiments.

FIG. 2A illustrates an example blockchain architecture configuration,according to example embodiments.

FIG. 2B illustrates a blockchain transactional flow, according toexample embodiments.

FIG. 3A illustrates a permissioned network, according to exampleembodiments.

FIG. 3B illustrates another permissioned network, according to exampleembodiments.

FIG. 3C illustrates a permissionless network, according to exampleembodiments.

FIG. 4 illustrates a system messaging diagram for validating gradientcalculations in a blockchain, according to example embodiments.

FIG. 5A illustrates a flow diagram of an example method of validating amachine learning computation in a blockchain, according to exampleembodiments.

FIG. 5B illustrates a flow diagram of an example method of validatingtraining aggregator computations in a blockchain, according to exampleembodiments.

FIG. 5C illustrates a flow diagram for validating model updates in ablockchain, according to example embodiments.

FIG. 6A illustrates an example system configured to perform one or moreoperations described herein, according to example embodiments.

FIG. 6B illustrates another example system configured to perform one ormore operations described herein, according to example embodiments.

FIG. 6C illustrates a further example system configured to utilize asmart contract, according to example embodiments.

FIG. 6D illustrates yet another example system configured to utilize ablockchain, according to example embodiments.

FIG. 7A illustrates a process for a new block being added to adistributed ledger, according to example embodiments.

FIG. 7B illustrates contents of a new data block, according to exampleembodiments.

FIG. 7C illustrates a blockchain for digital content, according toexample embodiments.

FIG. 7D illustrates a block which may represent the structure of blocksin the blockchain, according to example embodiments.

FIG. 8A illustrates an example blockchain which stores machine learning(artificial intelligence) data, according to example embodiments.

FIG. 8B illustrates an example quantum-secure blockchain, according toexample embodiments.

FIG. 9 illustrates an example system that supports one or more of theexample embodiments.

DETAILED DESCRIPTION

It will be readily understood that the instant components, as generallydescribed and illustrated in the figures herein, may be arranged anddesigned in a wide variety of different configurations. Thus, thefollowing detailed description of the embodiments of at least one of amethod, apparatus, non-transitory computer readable medium and system,as represented in the attached figures, is not intended to limit thescope of the application as claimed but is merely representative ofselected embodiments.

The instant features, structures, or characteristics as describedthroughout this specification may be combined or removed in any suitablemanner in one or more embodiments. For example, the usage of the phrases“example embodiments”, “some embodiments”, or other similar language,throughout this specification refers to the fact that a particularfeature, structure, or characteristic described in connection with theembodiment may be included in at least one embodiment. Thus, appearancesof the phrases “example embodiments”, “in some embodiments”, “in otherembodiments”, or other similar language, throughout this specificationdo not necessarily all refer to the same group of embodiments, and thedescribed features, structures, or characteristics may be combined orremoved in any suitable manner in one or more embodiments. Further, inthe diagrams, any connection between elements can permit one-way and/ortwo-way communication even if the depicted connection is a one-way ortwo-way arrow. Also, any device depicted in the drawings can be adifferent device. For example, if a mobile device is shown sendinginformation, a wired device could also be used to send the information.

In addition, while the term “message” may have been used in thedescription of embodiments, the application may be applied to many typesof networks and data. Furthermore, while certain types of connections,messages, and signaling may be depicted in exemplary embodiments, theapplication is not limited to a certain type of connection, message, andsignaling.

Example embodiments provide methods, systems, components, non-transitorycomputer readable media, devices, and/or networks, which provideefficient database machine learning verification.

In one embodiment the application utilizes a decentralized database(such as a blockchain) that is a distributed storage system, whichincludes multiple nodes that communicate with each other. Thedecentralized database includes an append-only immutable data structureresembling a distributed ledger capable of maintaining records betweenmutually untrusted parties. The untrusted parties are referred to hereinas peers or peer nodes. Each peer maintains a copy of the databaserecords and no single peer can modify the database records without aconsensus being reached among the distributed peers. For example, thepeers may execute a consensus protocol to validate blockchain storagetransactions, group the storage transactions into blocks, and build ahash chain over the blocks. This process forms the ledger by orderingthe storage transactions, as is necessary, for consistency. In variousembodiments, a permissioned and/or a permissionless blockchain can beused. In a public or permission-less blockchain, anyone can participatewithout a specific identity. Public blockchains can involve nativecryptocurrency and use consensus based on various protocols such asProof of Work (PoW). On the other hand, a permissioned blockchaindatabase provides secure interactions among a group of entities whichshare a common goal but which do not fully trust one another, such asbusinesses that exchange funds, goods, information, and the like.

This application can utilize a blockchain that operates arbitrary,programmable logic, tailored to a decentralized storage scheme andreferred to as “smart contracts” or “chaincodes.” In some cases,specialized chaincodes may exist for management functions and parameterswhich are referred to as system chaincode. The application can furtherutilize smart contracts that are trusted distributed applications whichleverage tamper-proof properties of the blockchain database and anunderlying agreement between nodes, which is referred to as anendorsement or endorsement policy. Blockchain transactions associatedwith this application can be “endorsed” before being committed to theblockchain while transactions, which are not endorsed, are disregarded.An endorsement policy allows chaincode to specify endorsers for atransaction in the form of a set of peer nodes that are necessary forendorsement. When a client sends the transaction to the peers specifiedin the endorsement policy, the transaction is executed to validate thetransaction. After validation, the transactions enter an ordering phasein which a consensus protocol is used to produce an ordered sequence ofendorsed transactions grouped into blocks.

This application can utilize nodes that are the communication entitiesof the blockchain system. A “node” may perform a logical function in thesense that multiple nodes of different types can run on the samephysical server. Nodes are grouped in trust domains and are associatedwith logical entities that control them in various ways. Nodes mayinclude different types, such as a client or submitting-client nodewhich submits a transaction-invocation to an endorser (e.g., peer), andbroadcasts transaction-proposals to an ordering service (e.g., orderingnode). Another type of node is a peer node which can receive clientsubmitted transactions, commit the transactions and maintain a state anda copy of the ledger of blockchain transactions. Peers can also have therole of an endorser, although it is not a requirement. Anordering-service-node or orderer is a node running the communicationservice for all nodes, and which implements a delivery guarantee, suchas a broadcast to each of the peer nodes in the system when committingtransactions and modifying a world state of the blockchain, which isanother name for the initial blockchain transaction which normallyincludes control and setup information.

This application can utilize a shared ledger that is a sequenced,tamper-resistant record of all state transitions of a blockchain. Statetransitions may result from chaincode invocations (i.e., transactions)submitted by participating parties (e.g., client nodes, ordering nodes,endorser nodes, peer nodes, etc.). Each participating party (such as apeer node) can maintain a copy of the ledger. A transaction may resultin a set of asset key-value pairs being committed to the ledger as oneor more operands, such as creates, updates, deletes, and the like. Theledger includes a blockchain (also referred to as a chain) which is usedto store an immutable, sequenced record in blocks. The shared ledgeralso includes a state database which maintains a current state of theblockchain.

This application can utilize a chain that is a transaction log which isstructured as hash-linked blocks, and each block contains a sequence ofN transactions where N is equal to or greater than one. The block headerincludes a hash of the block's transactions, as well as a hash of theprior block's header. In this way, all transactions on the ledger may besequenced and cryptographically linked together. Accordingly, it is notpossible to tamper with the ledger data without breaking the hash links.A hash of a most recently added blockchain block represents everytransaction on the chain that has come before it, making it possible toensure that all peer nodes are in a consistent and trusted state. Thechain may be stored on a peer node file system (i.e., local, attachedstorage, cloud, etc.), efficiently supporting the append-only nature ofthe blockchain workload.

The current state of the immutable ledger represents the latest valuesfor all keys that are included in the chain transaction log. Since thecurrent state represents the latest key values known to a channel, it issometimes referred to as a world state. Chaincode invocations executetransactions against the current state data of the ledger. To make thesechaincode interactions efficient, the latest values of the keys may bestored in a state database. The state database may be simply an indexedview into the chain's transaction log, it can therefore be regeneratedfrom the chain at any time. The state database may automatically berecovered (or generated if needed) upon peer node startup, and beforetransactions are accepted.

Some benefits of the instant solutions described and depicted hereininclude the following. The disclosed protocols provide efficientlyverifiable consensus mechanisms for the most popular algorithms inmachine learning and artificial intelligence (stochastic gradientdescent calculations and its variants). Verification of transactionproposals is several orders of magnitude faster (hours v/s seconds) thanconventional verification, based on approximate and provable correctverifications instead of re-compute processes. The disclosed embodimentsenable verifiability in different kinds of machine learning andartificial intelligence processes, including but not limited to popularmachine learning training, optimization, deep neural networks, andsupport vector machines (SVMs). The disclosed embodiments also providegreater efficiency than conventional approaches and enable real-timeauditability, by moving away from “post-process audit” throughre-computation to real-time computation guarantees.

The present application enables efficient (real-time) verification ofblockchain native machine learning and artificial intelligenceprocesses. For example, training deep neural nets involves severalgradient descent updates. The problem addressed by the presentapplication relates to current blockchain consensus processes. Currentblockchain consensus processes verify any computations via a “re-compute& compare” approach on each endorsing peer. In Hyperledger Fabricblockchain networks, the consensus mechanism involves multiple“endorsing peers” performing the same computation as the transactionsubmitting participant/peer and then comparing the results. The endorsernodes or peers arrive at the “consensus” if and only if a specifiednumber of them agree on the results. If conventional blockchainprocesses were used, gradient computation would be performed as part ofa smart contract on blockchain. Multiple endorsers would repeat thegradient computations for each iteration and compare & verify theresults. This is a wasteful and highly inefficient approach. The currentblockchain consensus processes are feasible only for simple andlightweight computations (e.g., simple financial operations). Forcomplex computations, such as gradient computations in machine learningand artificial intelligence processes, what is needed is a significantlymore efficient and computationally light approach.

The present application is about adding a capability to blockchaintechnology by enabling fast verification of machine learning trainingprocedures. Although it can be used without a blockchain, the primarybenefit of the disclosed verification protocols is intended to speed upmachine learning training on a blockchain. One could potentially achievea similar outcome if there is one fully trusted party that all otherparticipants fully trust to run the database and if all verifiers arehonest and can be trusted. This is not the case in many (or even most)practical settings.

The present application improves processing speed and resourceconsumption in blockchain networks, while training machine learningmodels on a blockchain. The novel protocol is designed to helpblockchain applications to efficiently verify machinelearning/artificial intelligence training updates. Sans the disclosedprotocol, if machine learning training procedures are posted as smartcontracts, they would trigger repetition of these procedures at theendorsers, thus delaying the consensus step. The disclosed embodimentsprovide novel and efficient verification mechanisms that do not repeatthe training procedures. This verification mechanism may be posted as asmart contract on behalf of the machine learning training procedure.

The present application proposes a new type of smart contract thataccompanies a machine learning training procedure. To this end, smartcontract transactions will have relevant details about parts of trainingdata and model updates. The improvement is precisely when an endorserverifies the training computation using this metadata. It does not needto perform the model training step again (which is a computationallyexpensive step). The disclosed embodiments describe a protocol that isan efficient verification step for endorser nodes or peers in blockchainnetworks.

FIG. 1A illustrates a block diagram of verify gradient transactionendorsement in a blockchain, according to example embodiments. Referringto FIG. 1A, the network 100 includes a training participation client104. A training participant client 104 is a blockchain client that isresponsible for training a machine learning model. Gradient computationis done by the training participant client 104 alone, since gradientcomputation by blockchain peers may be intractable due to limitedcomputing resources on common blockchain peers. Efficient gradientcomputation may require advanced computing resources including graphicsprocessing units (GPUs), which are more economically deployed inblockchain clients 104 that deal with the advanced calculationsdirectly.

The training participation client 104 may be part of a trainingparticipant network 112 that includes other training participant clients104. The training participation client 104 includes a training dataset108. The blockchain network 100 may be either a public or permissionedblockchain network 100. However, the disclosed processes are primarilyintended for permissioned blockchain networks 100.

Stochastic gradient descent is a standard optimization technique used intraining machine learning models. This technique involves computation ofthe so called “gradients” of a particular loss function defined on thetraining dataset 108 and the type of model involved. In technical terms,a gradient is a representation of the best improvement that can be madeto the model in a current iteration. In a typical machine learningtraining process, one starts from an initial model (intended forprediction) and updates the model step-by-step (i.e. iteratively),thereby guiding it in the direction of the best possible improvement perstep. At the end of the process, one expects to find a model that worksvery well on the dataset 108 used to train the model. This stepwiseupdate procedure is referred to as an iteration.

The disclosed processes provide efficiently verifiable consensusmechanisms and protocols for the most popular stochastic gradientdescent protocols and variants. These processes provide several ordersof magnitude faster computation (seconds vs. hours) based on approximateand provably correct verifications. They enable verifiability indifferent kinds of machine learning and artificial intelligenceprocesses, (e.g., popular machine learning training, optimization, etc.including the deep neural networks and support vector machines (SVMs)).The disclosed processes also provide real-time auditability, thus movingaway from “post-process audit” through re-computation to real-timecomputation guarantees.

In machine learning, during training, the model updating process mayrequire a very expensive procedure that calculates the gradient. Duringtest time, one just needs to do a prediction based on the current model.This prediction step is very fast. The disclosed novel methods use a fewfast prediction steps to verify the gradient computation in a singletraining step.

In some embodiments, the training participation network 112 may includea trusted blockchain node or peer (not shown), which directly interfaceswith blockchain nodes or peers providing endorsement, an orderingservice, and block committers. The blockchain network 100 includesvarious endorser nodes or peers 116, each of which includes a verifygradient smart contract 120. There may be any number of endorser nodesor peers 116 in the blockchain network 100. FIG. 1A illustrates Nendorser nodes or peers 116, from endorser node or peer 116A (includingverify gradient smart contract 120A) through from endorser node or peer116N (including verify gradient smart contract 120N).

The training participation client 104 performs gradient calculations onthe training dataset 108, and the training participant network 112provides the results in verify gradient transaction proposals 124 to theendorser nodes or peers 116. The endorser nodes or peers 116 execute theverify gradient smart contract 120 on the verify gradient transactionproposals 124. The verify gradient transaction proposals 124 that areapproved are returned as endorsements 128 to the training participationnetwork 112. Otherwise, the verify gradient transaction proposals 124that fail are rejected. In a blockchain context, endorsers 116 usuallyverify the computations in smart contracts. However, the exact trainingprocedure at the training participation client 104 cannot be included ina smart contract as it would trigger repetition of the computationallyexpensive training procedure, and thus causing significant delays withinthe blockchain network 100.

As an example, consider a single training participant client 104 withdata D trains a model M using a stochastic gradient descent or relatedprocess. The goal is to ensure that the model is being properly trainedby verifying gradients at each step or iteration. In a first step, thetraining participant client 104 accesses a batch B from D and computesthe latest gradients g_(t) on a loss function L, using previous modelparameters W_(t) and batch B. Data D and model parameters W_(t) belongto the training participant client 104 who is responsible for trainingthe model. These are stored outside the blockchain and only theverification-related attributes are stored on the blockchain. A typicalstep (iteration) in the training procedure takes the current modelparameters W_(t) and updates them using a small subset of samples fromthe training dataset 108. Subsequently, using some other subset ofsamples, further updates are made on the new model parameters. Any ofthese small subset of samples being used in a step is called a batch B.The previous model parameters W_(t) are parameters of an output model ofthe previous training step or iteration.

The gradient computation may be stated as: g_(t)=(x∈B) ∇L(W_(t);x) Theabove parameters are described in more detail with respect to the verifygradient smart contract discussion with respect to FIG. 1C.

The weights computation may be stated as: W_(t+1)=W_(t)−ηg_(t), where ηis a scalar value that decides the step size (also called the learningrate in machine learning), which is how much the current model should beupdated in the direction of the gradient

The blockchain verification (API call) is Verify Gradient (W_(t), g_(t),B). In a second step, a new verify gradient transaction proposal 124 issubmitted to the blockchain network 100, with previous model parametersW_(t), gradient g_(t), and the batch B. In a third step, one or moreendorser nodes or peers 116 execute the verify gradient smart contract120 in order to verify the correctness of the gradient computation bythe training participant client 104, and return endorsements 128.

FIG. 1B illustrates a block diagram of a training participant network112, according to example embodiments. Referring to FIG. 1B, thetraining participant network 112 may include a group of trainingparticipant clients 104, each including a training datatset 108. Thetraining participant network 112 may include any number of trainingparticipant clients 104, and FIG. 1B illustrates M training participantclients 104 in the training participant network 112. Trainingparticipant client 104A includes training dataset 108A, trainingparticipant client 104B includes training dataset 108B, and trainingparticipant client 104M includes training dataset 108M.

In one embodiment, such as Google's federated learning, a collaborativeprocess may be required, where training participants 104 train a commonmodel. The training aggregator 132 collects gradient calculations 136from the training participants 104 and combines them to create anaggregate model. The training aggregator 132 may be a special type oftraining participant 104 that is responsible for collecting inputs (suchas gradients) from different training participants 104 and apply them tothe model being collectively trained. It is desirable to enableverification of updates in this distributed machine learning setting inorder to ensure that no training participant 104 is cheating. Modelparameters 136 specify/describe a model in mathematical terms. Theseparameters 136 are used during prediction. In collaborative machinelearning applications, there may be multiple training participants 104contributing their own individual datasets 108.

Each of the training participant clients 104 provides gradientcalculations 136 to the training aggregator 132. The gradientcalculations 136 are performed by each of the training participantclients 104 against its own training dataset 108. The trainingaggregator 132 in one embodiment is a blockchain client, and in otherembodiments may include a trusted blockchain node or peer. Theillustrated embodiment assumes the training aggregator 132 is also atrusted blockchain node or peer. The training aggregator generatesverify gradient transaction proposals 124 in response to the receivedgradient calculations 136.

FIG. 1C illustrates a block diagram of an alternate embodiment of agradient calculation verification system 150, according to exampleembodiments. Referring to FIG. 1B, the training participant network 150may include endorser nodes or peers 116 that may not include the verifygradient smart contract 120. Instead, a trusted independent auditor 154may be accessible by the endorser nodes or peers 116. The independentauditor 154 is an auditing entity (such as a government agency)responsible in verifying the progress of a machine learning trainingprocess. Endorser nodes or peers 116 may refer to an independent auditor154 to execute the verify gradient smart contract 120 and verifygradient computation steps. In response to receiving the gradientcomputation transaction proposals 124 from the training participantnetwork 112, the endorser nodes or peer 116 forwards the gradientcomputation transaction proposals 162 to the independent auditor 154.

The independent auditor 154 executes the verify gradient smart contract120 on the received forwarded gradient computation transaction proposals162. The verification results 166 may include endorsements or rejectedtransaction proposals, and are returned to the endorser nodes or peers116, where the verification results 166 are eventually included in theshared ledger 128 of the gradient calculation verification system 150.Each endorser node or peer 116 includes a copy of the shared ledger 158,with endorser node or peer 116A including shared ledger 158A andendorser node or peer 116N including shared ledger 158N.

The verify gradient smart contract 120 performs the following steps:

First, the verify gradient smart contract 120 chooses a random scalar μand a random unit vector μ. Next, the verify gradient smart contract 120uses the following formula to verify correctness of the gradients:(W _(t) ,g _(t) ,β,B)=|Σ(x∈B)L(W _(t) +βμ;x)−L(W _(t) ;x)−|B|β(g^(T)μ)|<τ  Verify gradient function

-   -   W_(t) refers to model parameters (weight parameters)    -   g_(t) refers to the gradient g computed to the t_(th) iteration    -   Σ refers to a sum of what follows

x refers to one sample in a batch B used to initiate the smart contract

B refers to the batch, which is a subset of samples used to compute themodel update

L is the loss function, which represents a cost metric related toprediction accuracy that machine learning algorithms attempt tooptimize. The loss function L which is being optimized during traininghas two input arguments—one is the current model parameter W_(t) and theother is the sample x. “;” is a separator notation that separates twoarguments of the loss function

-   -   Since B is a set of points, |B| refers to the number of points        in B (cardinality of B)    -   g_(t) refers to the gradient g computed to the t_(th) iteration    -   g^(T) refers to a vector transpose operation used to compute        element-wise products of two vectors    -   τ is a small suitable threshold parameter. τ may be set to β²* a        smoothness parameter.    -   The smoothness parameter is that of the loss function L and is        application dependent.    -   The smoothness parameter is an upper bound on the absolute value        of eigenvalues of the Hessian of the loss function L

In order to verify correctness of the gradient calculation, the verifygradient smart contract 120 applies Taylor's expansion on the lossfunction L using the current model parameters & at a small perturbationof the model parameters in the random direction chosen and random stepsize chosen. A small perturbation reflects a small change in the valuesof model parameters. The random direction refers to the random unitvector μ. This is chosen by the verify gradient smart contract 120. Thestep size is the random scalar β, which is also chosen by the verifygradient smart contract 120. Given that the step size β is small, theabove formula holds. A small step size means that the scalar β has to bevery sufficiently small in magnitude to other parameters.

FIG. 2A illustrates a blockchain architecture configuration 200,according to example embodiments. Referring to FIG. 2A, the blockchainarchitecture 200 may include certain blockchain elements, for example, agroup of blockchain nodes 202. The blockchain nodes 202 may include oneor more nodes 204-210 (these four nodes are depicted by example only).These nodes participate in a number of activities, such as blockchaintransaction addition and validation process (consensus). One or more ofthe blockchain nodes 204-210 may endorse transactions based onendorsement policy and may provide an ordering service for allblockchain nodes in the architecture 200. A blockchain node may initiatea blockchain authentication and seek to write to a blockchain immutableledger stored in blockchain layer 216, a copy of which may also bestored on the underpinning physical infrastructure 214. The blockchainconfiguration may include one or more applications 224 which are linkedto application programming interfaces (APIs) 222 to access and executestored program/application code 220 (e.g., chaincode, smart contracts,etc.) which can be created according to a customized configurationsought by participants and can maintain their own state, control theirown assets, and receive external information. This can be deployed as atransaction and installed, via appending to the distributed ledger, onall blockchain nodes 204-210.

The blockchain base or platform 212 may include various layers ofblockchain data, services (e.g., cryptographic trust services, virtualexecution environment, etc.), and underpinning physical computerinfrastructure that may be used to receive and store new transactionsand provide access to auditors which are seeking to access data entries.The blockchain layer 216 may expose an interface that provides access tothe virtual execution environment necessary to process the program codeand engage the physical infrastructure 214. Cryptographic trust services218 may be used to verify transactions such as asset exchangetransactions and keep information private.

The blockchain architecture configuration of FIG. 2A may process andexecute program/application code 220 via one or more interfaces exposed,and services provided, by blockchain platform 212. The code 220 maycontrol blockchain assets. For example, the code 220 can store andtransfer data, and may be executed by nodes 204-210 in the form of asmart contract and associated chaincode with conditions or other codeelements subject to its execution. As a non-limiting example, smartcontracts may be created to execute reminders, updates, and/or othernotifications subject to the changes, updates, etc. The smart contractscan themselves be used to identify rules associated with authorizationand access requirements and usage of the ledger. For example, theinformation 226 may include gradient calculations from one or moretraining participant clients and may be processed by one or moreprocessing entities (e.g., virtual machines) included in the blockchainlayer 216. The result 228 may include endorsed transaction proposalsfrom executing a verify gradient smart contract. The physicalinfrastructure 214 may be utilized to retrieve any of the data orinformation described herein.

A smart contract may be created via a high-level application andprogramming language, and then written to a block in the blockchain. Thesmart contract may include executable code which is registered, stored,and/or replicated with a blockchain (e.g., distributed network ofblockchain peers). A transaction is an execution of the smart contractcode which can be performed in response to conditions associated withthe smart contract being satisfied. The executing of the smart contractmay trigger a trusted modification(s) to a state of a digital blockchainledger. The modification(s) to the blockchain ledger caused by the smartcontract execution may be automatically replicated throughout thedistributed network of blockchain peers through one or more consensusprotocols.

The smart contract may write data to the blockchain in the format ofkey-value pairs. Furthermore, the smart contract code can read thevalues stored in a blockchain and use them in application operations.The smart contract code can write the output of various logic operationsinto the blockchain. The code may be used to create a temporary datastructure in a virtual machine or other computing platform. Data writtento the blockchain can be public and/or can be encrypted and maintainedas private. The temporary data that is used/generated by the smartcontract is held in memory by the supplied execution environment, thendeleted once the data needed for the blockchain is identified.

A chaincode may include the code interpretation of a smart contract,with additional features. As described herein, the chaincode may beprogram code deployed on a computing network, where it is executed andvalidated by chain validators together during a consensus process. Thechaincode receives a hash and retrieves from the blockchain a hashassociated with the data template created by use of a previously storedfeature extractor. If the hashes of the hash identifier and the hashcreated from the stored identifier template data match, then thechaincode sends an authorization key to the requested service. Thechaincode may write to the blockchain data associated with thecryptographic details.

FIG. 2B illustrates an example of a blockchain transactional flow 250between nodes of the blockchain in accordance with an exampleembodiment. Referring to FIG. 2B, the transaction flow may include atransaction proposal 291 sent by an application client node 260 to anendorsing peer node 281. The endorsing peer 281 may verify the clientsignature and execute a chaincode function to initiate the transaction.The output may include the chaincode results, a set of key/valueversions that were read in the chaincode (read set), and the set ofkeys/values that were written in chaincode (write set). The proposalresponse 292 is sent back to the client 260 along with an endorsementsignature, if approved. The client 260 assembles the endorsements into atransaction payload 293 and broadcasts it to an ordering service node284. The ordering service node 284 then delivers ordered transactions asblocks to all peers 281-283 on a channel. Before committal to theblockchain, each peer 281-283 may validate the transaction. For example,the peers may check the endorsement policy to ensure that the correctallotment of the specified peers have signed the results andauthenticated the signatures against the transaction payload 293.

Referring again to FIG. 2B, the client node 260 initiates thetransaction 291 by constructing and sending a request to the peer node281, which is an endorser. The client 260 may include an applicationleveraging a supported software development kit (SDK), which utilizes anavailable API to generate a transaction proposal. The proposal is arequest to invoke a chaincode function so that data can be read and/orwritten to the ledger (i.e., write new key value pairs for the assets).The SDK may serve as a shim to package the transaction proposal into aproperly architected format (e.g., protocol buffer over a remoteprocedure call (RPC)) and take the client's cryptographic credentials toproduce a unique signature for the transaction proposal.

In response, the endorsing peer node 281 may verify (a) that thetransaction proposal is well formed, (b) the transaction has not beensubmitted already in the past (replay-attack protection), (c) thesignature is valid, and (d) that the submitter (client 260, in theexample) is properly authorized to perform the proposed operation onthat channel. The endorsing peer node 281 may take the transactionproposal inputs as arguments to the invoked chaincode function. Thechaincode is then executed against a current state database to producetransaction results including a response value, read set, and write set.However, no updates are made to the ledger at this point. In 292, theset of values along with the endorsing peer node's 281 signature ispassed back as a proposal response 292 to the SDK of the client 260which parses the payload for the application to consume.

In response, the application of the client 260 inspects/verifies theendorsing peers signatures and compares the proposal responses todetermine if the proposal response is the same. If the chaincode onlyqueried the ledger, the application would inspect the query response andwould typically not submit the transaction to the ordering node service284. If the client application intends to submit the transaction to theordering node service 284 to update the ledger, the applicationdetermines if the specified endorsement policy has been fulfilled beforesubmitting (i.e., did all peer nodes necessary for the transactionendorse the transaction). Here, the client may include only one ofmultiple parties to the transaction. In this case, each client may havetheir own endorsing node, and each endorsing node will need to endorsethe transaction. The architecture is such that even if an applicationselects not to inspect responses or otherwise forwards an unendorsedtransaction, the endorsement policy will still be enforced by peers andupheld at the commit validation phase.

After successful inspection, in step 293 the client 260 assemblesendorsements into a transaction and broadcasts the transaction proposaland response within a transaction message to the ordering node 284. Thetransaction may contain the read/write sets, the endorsing peerssignatures and a channel ID. The ordering node 284 does not need toinspect the entire content of a transaction in order to perform itsoperation, instead the ordering node 284 may simply receive transactionsfrom all channels in the network, order them chronologically by channel,and create blocks of transactions per channel.

The blocks of the transaction are delivered from the ordering node 284to all peer nodes 281-283 on the channel. The transactions 294 withinthe block are validated to ensure any endorsement policy is fulfilledand to ensure that there have been no changes to ledger state for readset variables since the read set was generated by the transactionexecution. Transactions in the block are tagged as being valid orinvalid. Furthermore, in step 295 each peer node 281-283 appends theblock to the channel's chain, and for each valid transaction the writesets are committed to current state database. An event is emitted, tonotify the client application that the transaction (invocation) has beenimmutably appended to the chain, as well as to notify whether thetransaction was validated or invalidated.

FIG. 3A illustrates an example of a permissioned blockchain network 300,which features a distributed, decentralized peer-to-peer architecture.In this example, a blockchain user 302 may initiate a transaction to thepermissioned blockchain 304. In this example, the transaction can be adeploy, invoke, or query, and may be issued through a client-sideapplication leveraging an SDK, directly through an API, etc. Networksmay provide access to a regulator 306, such as an auditor. A blockchainnetwork operator 308 manages member permissions, such as enrolling theregulator 306 as an “auditor” and the blockchain user 302 as a “client”.An auditor could be restricted only to querying the ledger whereas aclient could be authorized to deploy, invoke, and query certain types ofchaincode.

A blockchain developer 310 can write chaincode and client-sideapplications. The blockchain developer 310 can deploy chaincode directlyto the network through an interface. To include credentials from atraditional data source 312 in chaincode, the developer 310 could use anout-of-band connection to access the data. In this example, theblockchain user 302 connects to the permissioned blockchain 304 througha peer node 314. Before proceeding with any transactions, the peer node314 retrieves the user's enrollment and transaction certificates from acertificate authority 316, which manages user roles and permissions. Insome cases, blockchain users must possess these digital certificates inorder to transact on the permissioned blockchain 304. Meanwhile, a userattempting to utilize chaincode may be required to verify theircredentials on the traditional data source 312. To confirm the user'sauthorization, chaincode can use an out-of-band connection to this datathrough a traditional processing platform 318.

FIG. 3B illustrates another example of a permissioned blockchain network320, which features a distributed, decentralized peer-to-peerarchitecture. In this example, a blockchain user 322 may submit atransaction to the permissioned blockchain 324. In this example, thetransaction can be a deploy, invoke, or query, and may be issued througha client-side application leveraging an SDK, directly through an API,etc. Networks may provide access to a regulator 326, such as an auditor.A blockchain network operator 328 manages member permissions, such asenrolling the regulator 326 as an “auditor” and the blockchain user 322as a “client”. An auditor could be restricted only to querying theledger whereas a client could be authorized to deploy, invoke, and querycertain types of chaincode.

A blockchain developer 330 writes chaincode and client-sideapplications. The blockchain developer 330 can deploy chaincode directlyto the network through an interface. To include credentials from atraditional data source 332 in chaincode, the developer 330 could use anout-of-band connection to access the data. In this example, theblockchain user 322 connects to the network through a peer node 334.Before proceeding with any transactions, the peer node 334 retrieves theuser's enrollment and transaction certificates from the certificateauthority 336. In some cases, blockchain users must possess thesedigital certificates in order to transact on the permissioned blockchain324. Meanwhile, a user attempting to utilize chaincode may be requiredto verify their credentials on the traditional data source 332. Toconfirm the user's authorization, chaincode can use an out-of-bandconnection to this data through a traditional processing platform 338.

In some embodiments, the blockchain herein may be a permissionlessblockchain. In contrast with permissioned blockchains which requirepermission to join, anyone can join a permissionless blockchain. Forexample, to join a permissionless blockchain a user may create apersonal address and begin interacting with the network, by submittingtransactions, and hence adding entries to the ledger. Additionally, allparties have the choice of running a node on the system and employingthe mining protocols to help verify transactions.

FIG. 3C illustrates a process 350 of a transaction being processed by apermissionless blockchain 352 including a plurality of nodes 354. Asender 356 desires to send payment or some other form of value (e.g., adeed, medical records, a contract, a good, a service, or any other assetthat can be encapsulated in a digital record) to a recipient 358 via thepermissionless blockchain 352. In one embodiment, each of the senderdevice 356 and the recipient device 358 may have digital wallets(associated with the blockchain 352) that provide user interfacecontrols and a display of transaction parameters. In response, thetransaction is broadcast throughout the blockchain 352 to the nodes 354.Depending on the blockchain's 352 network parameters the nodes verify360 the transaction based on rules (which may be pre-defined ordynamically allocated) established by the permissionless blockchain 352creators. For example, this may include verifying identities of theparties involved, etc. The transaction may be verified immediately or itmay be placed in a queue with other transactions and the nodes 354determine if the transactions are valid based on a set of network rules.

In structure 362, valid transactions are formed into a block and sealedwith a lock (hash). This process may be performed by mining nodes amongthe nodes 354. Mining nodes may utilize additional software specificallyfor mining and creating blocks for the permissionless blockchain 352.Each block may be identified by a hash (e.g., 256 bit number, etc.)created using an algorithm agreed upon by the network. Each block mayinclude a header, a pointer or reference to a hash of a previous block'sheader in the chain, and a group of valid transactions. The reference tothe previous block's hash is associated with the creation of the secureindependent chain of blocks.

Before blocks can be added to the blockchain, the blocks must bevalidated. Validation for the permissionless blockchain 352 may includea proof-of-work (PoW) which is a solution to a puzzle derived from theblock's header. Although not shown in the example of FIG. 3C, anotherprocess for validating a block is proof-of-stake. Unlike theproof-of-work, where the algorithm rewards miners who solve mathematicalproblems, with the proof of stake, a creator of a new block is chosen ina deterministic way, depending on its wealth, also defined as “stake.”Then, a similar proof is performed by the selected/chosen node.

With mining 364, nodes try to solve the block by making incrementalchanges to one variable until the solution satisfies a network-widetarget. This creates the PoW thereby ensuring correct answers. In otherwords, a potential solution must prove that computing resources weredrained in solving the problem. In some types of permissionlessblockchains, miners may be rewarded with value (e.g., coins, etc.) forcorrectly mining a block.

Here, the PoW process, alongside the chaining of blocks, makesmodifications of the blockchain extremely difficult, as an attacker mustmodify all subsequent blocks in order for the modifications of one blockto be accepted. Furthermore, as new blocks are mined, the difficulty ofmodifying a block increases, and the number of subsequent blocksincreases. With distribution 366, the successfully validated block isdistributed through the permissionless blockchain 352 and all nodes 354add the block to a majority chain which is the permissionlessblockchain's 352 auditable ledger. Furthermore, the value in thetransaction submitted by the sender 356 is deposited or otherwisetransferred to the digital wallet of the recipient device 358.

FIG. 4 illustrates a system messaging diagram for validating gradientcalculations in a blockchain, according to example embodiments.Referring to FIG. 4 , the system diagram 400 includes training clients410, a training aggregator 420, and endorser nodes or peers 430. In theillustrated embodiment, it is assumed the training aggregator 420 isalso a trusted node or peer of the same blockchain network as theendorser nodes or peers 430. However, in other embodiments, the trainingaggregator 420 may be a client and communicate to the endorser nodes orpeers 430 through a separate trusted node or peer.

The training clients 410 first receive current model parameters 411 fromthe training aggregator 420. Each of the training clients 410 trains andperforms gradient calculations 415 on a local dataset, and transfers thegradient calculations and metadata 416 to the training aggregator 420.In turn, the training aggregator 420 generates transaction proposals 425for each of the received gradient calculations and metadata 416. Thetransaction proposals 426 are next sent to endorser nodes or peers 430of the blockchain network.

The endorser nodes or peers 430 receive the transaction proposals 426,and execute a verify gradient smart contract 435. The verify gradientsmart contract 435 obtains parameters from the transaction proposals 426and approves or rejects transaction proposals 426 by attempting toapprove the gradient calculations 416. If the verify gradient smartcontract is able to approve transaction proposals 426, it returnsendorsements 436 to the training aggregator 420, otherwise it returnsrejections. The training aggregator 420 gathers the endorsements 436from the endorser nodes or peers 430 and generates blockchaintransactions 440 that are then submitted 441 to the blockchain network.

The training aggregator 420 creates a new aggregate machine learningmodel by combining the endorsed gradients 445, and then generates newtransaction proposals 450.

FIG. 5A illustrates a flow diagram 500 of an example method ofvalidating a machine learning computation in a blockchain, according toexample embodiments. Referring to FIG. 5A, the method 500 may includeone or more of the following steps.

At block 502, a training participant client computes a gradient for amachine learning or artificial intelligence model training step.

At block 504, the training participant client submits a gradientcomputation transaction proposal to a blockchain network. In oneembodiment, the training participant client submits a request for thegradient computation to be verified to a trusted node or peer of theblockchain network. The trusted node or peer receives the request, andsubmits the corresponding transaction proposal to the blockchainnetwork.

At block 506, one or more endorser nodes or peers of the blockchainnetwork receive the transaction proposal, and execute a verify gradientsmart contract. The verify gradient smart contract evaluates the contentof the transaction proposal in order to determine if the transactionproposal is valid or not. In one embodiment, the endorser nodes or peersdo not include the verify gradient smart contract. In that case, theendorser nodes or peers forward the transaction proposals to one or moreindependent auditors outside the blockchain network. The independentcontractors include the verify gradient smart contract, and execute itagainst the transaction proposals to provide endorsement. In this case,the endorser nodes or peers would provide the endorsed transactionproposals to the training participant client or a trusted blockchainnode or peer that processes the endorsements.

At block 508, the endorser nodes or peers provide endorsements forapproved transaction proposals.

At block 512, the endorser nodes or peers reject unapproved transactionproposals.

FIG. 5B illustrates a flow diagram 520 of an example method ofvalidating training aggregator computations in a blockchain, accordingto example embodiments. Referring to FIG. 5B, the method 520 may includeone or more of the following steps.

At block 522, a group of training participant clients compute gradientsfor a machine learning or artificial intelligence training calculation.

At block 524, the group of training participant clients submit theoriginal model parameter W_(t) received from the a training aggregator,the new gradient computed on it, and the subset of their local data usedto the training aggregator.

At block 526, the training aggregator converts the gradient calculationsto transaction proposals, and submits transaction proposals to ablockchain network.

At block 528, one or more endorser nodes or peers receive thetransaction proposals, and execute a verify gradient smart contract tovalidate the transaction proposals. In one embodiment, the endorsernodes or peers do not include the verify gradient smart contract. Inthat case, the endorser nodes or peers forward the transaction proposalsto one or more independent auditors outside the blockchain network. Theindependent contractors include the verify gradient smart contract, andexecute it against the transaction proposals to provide endorsement. Inthis case, the endorser nodes or peers would provide the endorsedtransaction proposals to the training aggregator.

At block 530, the endorser nodes or peers provide endorsements for theapproved transaction proposals.

FIG. 5C illustrates a flow diagram 540 for validating model updates in ablockchain, according to example embodiments. Referring to FIG. 5C, themethod 540 may include one or more of the following steps.

At block 542, model update data is generated. In various embodiments,blockchain nodes may each be loaded with one or more computing modelsconfigured to perform various machine learning and artificialintelligence operations. In various embodiments, initial setup,installation, configuration, or the like of the computing models mayvary and be completed using any suitable technique. For example,participation in the system may be limited based on a defined list ofcertificates (where certificates are generated for new nodes joining asystem), may be restricted based on a private network configuration, maybe restricted based on issued blockchain addresses, or the like.

A node may detect a model update event. For example, a node may detectthe model update event while operating the computing model, through atransmission (e.g., an update is transmitted to the node), or the like.The model update event may comprise a prediction error, a new modelrequirement, a customization event, or the like. For example, theprediction error may relate to an incorrect identification using thecomputing model, or may be associated with any other suitable or desirederror. The new model requirement may relate to a new identificationneeded for the computing model. For example, wherein the computing modelis configured to recognize inventory in a store, a new model requirementmay be in response to a new good (e.g., soda, etc.) being offered asinventory in the store.

The node generates model update data based on the model update event.The model update data may comprise training data (e.g., data to beingested by a node to “train” the computing model to cure the predictionerror, detect the new model requirement, etc.), a model update (e.g.,programmable code to merge with the computing model to cure theprediction error, detect the new model requirement, etc.), and/or a newcomputing model. A node transmits the model update data to a validationnode.

At block 544, the model update data is validated. In variousembodiments, a validation node may transmit the model update data basedon a distribution algorithm or the like. For example, and in accordancewith various embodiments, delegation to a limited number of validationnodes may be controlled or established via a delegated proof of stakesystem. As a further example, delegation may be controlled by a smartcontract (e.g., a publish and subscribe model) where validation andmodel updating is performed by one or more validation nodes. As afurther example, delegation may be controlled by signed authorizationsused to establish relationships between the nodes. In variousembodiments, delegation to validation nodes may also be static anddefined, where each node may include instructions detailing one or morevalidation nodes to transmit the model update data to. In variousembodiments, one or more validation nodes may also publish contracts andoffer a payment or the like to validate and/or update the model updatedata. Nodes may select one or more validation nodes based on thepublished contracts.

In various embodiments, in response to a validation node (and/or aplurality of validation nodes) receiving a plurality of model updatedata transmission simultaneously, in near time, or the like, the orderof updating computing models may be based on a time stamp (e.g., timestamp present in the model update data, or a time stamp of when thevalidation node received the model update data). In various embodiments,the received plurality of model update data may also be aggregatedand/or averaged together using an averaging model or algorithm.

In response to receiving the model update data, the validation nodevalidates the model update data. The validation node may broadcast themodel update data to one or more other validation nodes in validationnetwork. Validation nodes may establish consensus to the model updatedata using any suitable or desired consensus method or algorithm, suchas, for example, proof of work, proof of stake, practical byzantinefault tolerance, delegated proof of stake, or any other suitableconsensus algorithm. Validation nodes may also be configured to validatethe model update data by locally testing the model update data todetermine whether the model update data cures the prediction error,detects the new model requirement, or the like. For example, validationnodes may locally implement the test data and/or model update, and mayprocess data using the computing model to determine whether theprediction error is still an issue, whether the new model requirementcan be detected, or the like. As a further example, validation nodes maytest the model update data to identity false positives, false negatives,or the like. Testing may also be performed by a human to identify newscenarios and/or to validate the model update data. Validation nodewrites the model update data to model blockchain.

The validation node may be configured to write the model update data tothe model blockchain together with any other suitable data, such as, forexample a hash of the model update data, a date or timestamp,identifying information of the node that transmitted the model updatedata (e.g., IP address, blockchain address, etc.), and/or any othersuitable data or metadata. The validation node propagates the write tothe validation network. For example, the validation node may broadcastthe write data (e.g., data associated with the block written to modelblockchain the model update data, etc.) to one or more validation nodesin validation network. Each validation node may be configured to writethe model update data to its associated local model blockchain.

At block 546, an updated computing model is generated. In variousembodiments, the validation node generates an updated computing modelbased on the model update data. For example, the validation node may beconfigured to generate the update computing model by merging thepreexisting computing model with the model update data. For example, thepreexisting computing model may be recomputed or retrained using the newtraining data, or parameters of the preexisting computing model may bechanged based on the model update data.

At block 548, the updated computing model is validated. The validationnode validates the updated computing model. The validation node maybroadcast the updated computing model to one or more other validationnodes in validation network. The validation nodes may establishconsensus to the updated computing model using any suitable or desiredconsensus method or algorithm, such as, for example, proof of work,proof of stake, practical byzantine fault tolerance, delegated proof ofstake, or any other suitable consensus algorithm. The validation nodesmay also be configured to validate the updated computing model bylocally testing the updated computing model to determine whether theupdated computing model cures the prediction error, detects the newmodel requirement, or the like. For example, the validation nodes maylocally implement the updated computing model, and may process datausing the computing model to determine whether the prediction errorstill results, to detect detects the new model requirement, or the like.The validation node writes the updated computing model to the modelblockchain. The validation node may be configured to write the updatedcomputing model to the model blockchain together with any other suitabledata, such as, for example a hash of the updated computing model, a dateor timestamp, identifying information of the node that transmitted themodel update data (e.g., IP address, blockchain address, etc.), and/orany other suitable data or metadata.

At block 550, the updated computing model is propagated. The validationnode propagates the write to the validation network. For example, thevalidation node may broadcast the write data (e.g., data associated withthe block written to the model blockchain, the updated computing model,etc.) to one or more validation nodes in the validation network. Eachvalidation node may be configured to write the updating computing modelto its associated local model blockchain. The validation node broadcaststhe updated computing model to node. In various embodiments, thevalidation node may broadcast the validated model update data to one ormore nodes, instead of the updated computing model. In variousembodiments, the validation node may broadcast the updated computingmodel based on a distribution algorithm or the like.

FIG. 6A illustrates an example system 600 that includes a physicalinfrastructure 610 configured to perform various operations according toexample embodiments. Referring to FIG. 6A, the physical infrastructure610 includes a module 612 and a module 614. The module 614 includes ablockchain 620 and a smart contract 630 (which may reside on theblockchain 620), that may execute any of the operational steps 608 (inmodule 612) included in any of the example embodiments. Thesteps/operations 608 may include one or more of the embodimentsdescribed or depicted and may represent output or written informationthat is written or read from one or more smart contracts 630 and/orblockchains 620. The physical infrastructure 610, the module 612, andthe module 614 may include one or more computers, servers, processors,memories, and/or wireless communication devices. Further, the module 612and the module 614 may be a same module.

FIG. 6B illustrates another example system 640 configured to performvarious operations according to example embodiments. Referring to FIG.6B, the system 640 includes a module 612 and a module 614. The module614 includes a blockchain 620 and a smart contract 630 (which may resideon the blockchain 620), that may execute any of the operational steps608 (in module 612) included in any of the example embodiments. Thesteps/operations 608 may include one or more of the embodimentsdescribed or depicted and may represent output or written informationthat is written or read from one or more smart contracts 630 and/orblockchains 620. The physical infrastructure 610, the module 612, andthe module 614 may include one or more computers, servers, processors,memories, and/or wireless communication devices. Further, the module 612and the module 614 may be a same module.

FIG. 6C illustrates an example system configured to utilize a smartcontract configuration among contracting parties and a mediating serverconfigured to enforce the smart contract terms on the blockchainaccording to example embodiments. Referring to FIG. 6C, theconfiguration 650 may represent a communication session, an assettransfer session or a process or procedure that is driven by a smartcontract 630 which explicitly identifies one or more user devices 652and/or 656. The execution, operations and results of the smart contractexecution may be managed by a server 654. Content of the smart contract630 may require digital signatures by one or more of the entities 652and 656 which are parties to the smart contract transaction. The resultsof the smart contract execution may be written to a blockchain 620 as ablockchain transaction. The smart contract 630 resides on the blockchain620 which may reside on one or more computers, servers, processors,memories, and/or wireless communication devices.

FIG. 6D illustrates a system 660 including a blockchain, according toexample embodiments. Referring to the example of FIG. 6D, an applicationprogramming interface (API) gateway 662 provides a common interface foraccessing blockchain logic (e.g., smart contract 630 or other chaincode)and data (e.g., distributed ledger, etc.). In this example, the APIgateway 662 is a common interface for performing transactions (invoke,queries, etc.) on the blockchain by connecting one or more entities 652and 656 to a blockchain peer (i.e., server 654). Here, the server 654 isa blockchain network peer component that holds a copy of the world stateand a distributed ledger allowing clients 652 and 656 to query data onthe world state as well as submit transactions into the blockchainnetwork where, depending on the smart contract 630 and endorsementpolicy, endorsing peers will run the smart contracts 630.

The above embodiments may be implemented in hardware, in a computerprogram executed by a processor, in firmware, or in a combination of theabove. A computer program may be embodied on a computer readable medium,such as a storage medium. For example, a computer program may reside inrandom access memory (“RAM”), flash memory, read-only memory (“ROM”),erasable programmable read-only memory (“EPROM”), electrically erasableprogrammable read-only memory (“EEPROM”), registers, hard disk, aremovable disk, a compact disk read-only memory (“CD-ROM”), or any otherform of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such thatthe processor may read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anapplication specific integrated circuit (“ASIC”). In the alternative,the processor and the storage medium may reside as discrete components.

FIG. 7A illustrates a process 700 of a new block being added to adistributed ledger 720, according to example embodiments, and FIG. 7Billustrates contents of a new data block structure 730 for blockchain,according to example embodiments. Referring to FIG. 7A, clients (notshown) may submit transactions to blockchain nodes 711, 712, and/or 713.Clients may be instructions received from any source to enact activityon the blockchain 720. As an example, clients may be applications thatact on behalf of a requester, such as a device, person or entity topropose transactions for the blockchain. The plurality of blockchainpeers (e.g., blockchain nodes 711, 712, and 713) may maintain a state ofthe blockchain network and a copy of the distributed ledger 720.Different types of blockchain nodes/peers may be present in theblockchain network including endorsing peers which simulate and endorsetransactions proposed by clients and committing peers which verifyendorsements, validate transactions, and commit transactions to thedistributed ledger 720. In this example, the blockchain nodes 711, 712,and 713 may perform the role of endorser node, committer node, or both.

The distributed ledger 720 includes a blockchain which stores immutable,sequenced records in blocks, and a state database 724 (current worldstate) maintaining a current state of the blockchain 722. Onedistributed ledger 720 may exist per channel and each peer maintains itsown copy of the distributed ledger 720 for each channel of which theyare a member. The blockchain 722 is a transaction log, structured ashash-linked blocks where each block contains a sequence of Ntransactions. Blocks may include various components such as shown inFIG. 7B. The linking of the blocks (shown by arrows in FIG. 7A) may begenerated by adding a hash of a prior block's header within a blockheader of a current block. In this way, all transactions on theblockchain 722 are sequenced and cryptographically linked togetherpreventing tampering with blockchain data without breaking the hashlinks. Furthermore, because of the links, the latest block in theblockchain 722 represents every transaction that has come before it. Theblockchain 722 may be stored on a peer file system (local or attachedstorage), which supports an append-only blockchain workload.

The current state of the blockchain 722 and the distributed ledger 722may be stored in the state database 724. Here, the current state datarepresents the latest values for all keys ever included in the chaintransaction log of the blockchain 722. Chaincode invocations executetransactions against the current state in the state database 724. Tomake these chaincode interactions extremely efficient, the latest valuesof all keys are stored in the state database 724. The state database 724may include an indexed view into the transaction log of the blockchain722, it can therefore be regenerated from the chain at any time. Thestate database 724 may automatically get recovered (or generated ifneeded) upon peer startup, before transactions are accepted.

Endorsing nodes receive transactions from clients and endorse thetransaction based on simulated results. Endorsing nodes hold smartcontracts which simulate the transaction proposals. When an endorsingnode endorses a transaction, the endorsing nodes creates a transactionendorsement which is a signed response from the endorsing node to theclient application indicating the endorsement of the simulatedtransaction. The method of endorsing a transaction depends on anendorsement policy which may be specified within chaincode. An exampleof an endorsement policy is “the majority of endorsing peers mustendorse the transaction”. Different channels may have differentendorsement policies. Endorsed transactions are forward by the clientapplication to ordering service 710.

The ordering service 710 accepts endorsed transactions, orders them intoa block, and delivers the blocks to the committing peers. For example,the ordering service 710 may initiate a new block when a threshold oftransactions has been reached, a timer times out, or another condition.In the example of FIG. 7A, blockchain node 712 is a committing peer thathas received a new data new data block 730 for storage on blockchain720. The first block in the blockchain may be referred to as a genesisblock which includes information about the blockchain, its members, thedata stored therein, etc.

The ordering service 710 may be made up of a cluster of orderers. Theordering service 710 does not process transactions, smart contracts, ormaintain the shared ledger. Rather, the ordering service 710 may acceptthe endorsed transactions and specifies the order in which thosetransactions are committed to the distributed ledger 720. Thearchitecture of the blockchain network may be designed such that thespecific implementation of ‘ordering’ (e.g., Solo, Kafka, BFT, etc.)becomes a pluggable component.

Transactions are written to the distributed ledger 720 in a consistentorder. The order of transactions is established to ensure that theupdates to the state database 724 are valid when they are committed tothe network. Unlike a cryptocurrency blockchain system (e.g., Bitcoin,etc.) where ordering occurs through the solving of a cryptographicpuzzle, or mining, in this example the parties of the distributed ledger720 may choose the ordering mechanism that best suits that network.

When the ordering service 710 initializes a new data block 730, the newdata block 730 may be broadcast to committing peers (e.g., blockchainnodes 711, 712, and 713). In response, each committing peer validatesthe transaction within the new data block 730 by checking to make surethat the read set and the write set still match the current world statein the state database 724. Specifically, the committing peer candetermine whether the read data that existed when the endorserssimulated the transaction is identical to the current world state in thestate database 724. When the committing peer validates the transaction,the transaction is written to the blockchain 722 on the distributedledger 720, and the state database 724 is updated with the write datafrom the read-write set. If a transaction fails, that is, if thecommitting peer finds that the read-write set does not match the currentworld state in the state database 724, the transaction ordered into ablock will still be included in that block, but it will be marked asinvalid, and the state database 724 will not be updated.

Referring to FIG. 7B, a new data block 730 (also referred to as a datablock) that is stored on the blockchain 722 of the distributed ledger720 may include multiple data segments such as a block header 740, blockdata 750, and block metadata 760. It should be appreciated that thevarious depicted blocks and their contents, such as new data block 730and its contents. shown in FIG. 7B are merely examples and are not meantto limit the scope of the example embodiments. The new data block 730may store transactional information of N transaction(s) (e.g., 1, 10,100, 500, 1000, 2000, 3000, etc.) within the block data 750. The newdata block 730 may also include a link to a previous block (e.g., on theblockchain 722 in FIG. 7A) within the block header 740. In particular,the block header 740 may include a hash of a previous block's header.The block header 740 may also include a unique block number, a hash ofthe block data 750 of the new data block 730, and the like. The blocknumber of the new data block 730 may be unique and assigned in variousorders, such as an incremental/sequential order starting from zero.

The block data 750 may store transactional information of eachtransaction that is recorded within the new data block 730. For example,the transaction data may include one or more of a type of thetransaction, a version, a timestamp, a channel ID of the distributedledger 720, a transaction ID, an epoch, a payload visibility, achaincode path (deploy tx), a chaincode name, a chaincode version, input(chaincode and functions), a client (creator) identify such as a publickey and certificate, a signature of the client, identities of endorsers,endorser signatures, a proposal hash, chaincode events, response status,namespace, a read set (list of key and version read by the transaction,etc.), a write set (list of key and value, etc.), a start key, an endkey, a list of keys, a Merkel tree query summary, and the like. Thetransaction data may be stored for each of the N transactions.

In some embodiments, the block data 750 may also store new data 762which adds additional information to the hash-linked chain of blocks inthe blockchain 722. The additional information includes one or more ofthe steps, features, processes and/or actions described or depictedherein. Accordingly, the new data 762 can be stored in an immutable logof blocks on the distributed ledger 720. Some of the benefits of storingsuch new data 762 are reflected in the various embodiments disclosed anddepicted herein. Although in FIG. 7B the new data 762 is depicted in theblock data 750 but could also be located in the block header 740 or theblock metadata 760.

The block metadata 760 may store multiple fields of metadata (e.g., as abyte array, etc.). Metadata fields may include signature on blockcreation, a reference to a last configuration block, a transactionfilter identifying valid and invalid transactions within the block, lastoffset persisted of an ordering service that ordered the block, and thelike. The signature, the last configuration block, and the orderermetadata may be added by the ordering service 710. Meanwhile, acommitter of the block (such as blockchain node 712) may addvalidity/invalidity information based on an endorsement policy,verification of read/write sets, and the like. The transaction filtermay include a byte array of a size equal to the number of transactionsin the block data 750 and a validation code identifying whether atransaction was valid/invalid.

FIG. 7C illustrates an embodiment of a blockchain 770 for digitalcontent in accordance with the embodiments described herein. The digitalcontent may include one or more files and associated information. Thefiles may include media, images, video, audio, text, links, graphics,animations, web pages, documents, or other forms of digital content. Theimmutable, append-only aspects of the blockchain serve as a safeguard toprotect the integrity, validity, and authenticity of the digitalcontent, making it suitable use in legal proceedings where admissibilityrules apply or other settings where evidence is taken in toconsideration or where the presentation and use of digital informationis otherwise of interest. In this case, the digital content may bereferred to as digital evidence.

The blockchain may be formed in various ways. In one embodiment, thedigital content may be included in and accessed from the blockchainitself. For example, each block of the blockchain may store a hash valueof reference information (e.g., header, value, etc.) along theassociated digital content. The hash value and associated digitalcontent may then be encrypted together. Thus, the digital content ofeach block may be accessed by decrypting each block in the blockchain,and the hash value of each block may be used as a basis to reference aprevious block. This may be illustrated as follows:

Block 1 Block 2 . . . Block N Hash Value 1 Hash Value 2 Hash Value NDigital Content 1 Digital Content 2 Digital Content N

In one embodiment, the digital content may be not included in theblockchain. For example, the blockchain may store the encrypted hashesof the content of each block without any of the digital content. Thedigital content may be stored in another storage area or memory addressin association with the hash value of the original file. The otherstorage area may be the same storage device used to store the blockchainor may be a different storage area or even a separate relationaldatabase. The digital content of each block may be referenced oraccessed by obtaining or querying the hash value of a block of interestand then looking up that has value in the storage area, which is storedin correspondence with the actual digital content. This operation may beperformed, for example, a database gatekeeper. This may be illustratedas follows:

Blockchain Storage Area Block 1 Hash Value Block 1 Hash Value . . .Content . . . . . . Block N Hash Value Block N Hash Value . . . Content

In the example embodiment of FIG. 7C, the blockchain 770 includes anumber of blocks 778 ₁, 778 ₂, . . . 778 _(N) cryptographically linkedin an ordered sequence, where N≥1. The encryption used to link theblocks 778 ₁, 778 ₂, . . . 778 _(N) may be any of a number of keyed orun-keyed Hash functions. In one embodiment, the blocks 778 ₁, 778 ₂, . .. 778 _(N) are subject to a hash function which produces n-bitalphanumeric outputs (where n is 256 or another number) from inputs thatare based on information in the blocks. Examples of such a hash functioninclude, but are not limited to, a SHA-type (SHA stands for Secured HashAlgorithm) algorithm, Merkle-Damgard algorithm, HAIFA algorithm,Merkle-tree algorithm, nonce-based algorithm, and anon-collision-resistant PRF algorithm. In another embodiment, the blocks778 ₁, 778 ₂, . . . , 778 _(N) may be cryptographically linked by afunction that is different from a hash function. For purposes ofillustration, the following description is made with reference to a hashfunction, e.g., SHA-2.

Each of the blocks 778 ₁, 778 ₂, . . . , 778 _(N) in the blockchainincludes a header, a version of the file, and a value. The header andthe value are different for each block as a result of hashing in theblockchain. In one embodiment, the value may be included in the header.As described in greater detail below, the version of the file may be theoriginal file or a different version of the original file.

The first block 778 ₁ in the blockchain is referred to as the genesisblock and includes the header 772 ₁, original file 774 ₁, and an initialvalue 776 ₁. The hashing scheme used for the genesis block, and indeedin all subsequent blocks, may vary. For example, all the information inthe first block 778 ₁ may be hashed together and at one time, or each ora portion of the information in the first block 778 ₁ may be separatelyhashed and then a hash of the separately hashed portions may beperformed.

The header 772 ₁ may include one or more initial parameters, which, forexample, may include a version number, timestamp, nonce, rootinformation, difficulty level, consensus protocol, duration, mediaformat, source, descriptive keywords, and/or other informationassociated with original file 774 ₁ and/or the blockchain. The header772 ₁ may be generated automatically (e.g., by blockchain networkmanaging software) or manually by a blockchain participant. Unlike theheader in other blocks 778 ₂ to 778 _(N) in the blockchain, the header772 ₁ in the genesis block does not reference a previous block, simplybecause there is no previous block.

The original file 774 ₁ in the genesis block may be, for example, dataas captured by a device with or without processing prior to itsinclusion in the blockchain. The original file 774 ₁ is received throughthe interface of the system from the device, media source, or node. Theoriginal file 774 ₁ is associated with metadata, which, for example, maybe generated by a user, the device, and/or the system processor, eithermanually or automatically. The metadata may be included in the firstblock 778 ₁ in association with the original file 774 ₁.

The value 776 ₁ in the genesis block is an initial value generated basedon one or more unique attributes of the original file 774 ₁. In oneembodiment, the one or more unique attributes may include the hash valuefor the original file 774 ₁, metadata for the original file 774 ₁, andother information associated with the file. In one implementation, theinitial value 776 ₁ may be based on the following unique attributes:

-   -   1) SHA-2 computed hash value for the original file    -   2) originating device ID    -   3) starting timestamp for the original file    -   4) initial storage location of the original file    -   5) blockchain network member ID for software to currently        control the original file and associated metadata

The other blocks 778 ₂ to 778 _(N) in the blockchain also have headers,files, and values. However, unlike the first block 772 ₁, each of theheaders 772 ₂ to 772 _(N) in the other blocks includes the hash value ofan immediately preceding block. The hash value of the immediatelypreceding block may be just the hash of the header of the previous blockor may be the hash value of the entire previous block. By including thehash value of a preceding block in each of the remaining blocks, a tracecan be performed from the Nth block back to the genesis block (and theassociated original file) on a block-by-block basis, as indicated byarrows 780, to establish an auditable and immutable chain-of-custody.

Each of the header 772 ₂ to 772 _(N) in the other blocks may alsoinclude other information, e.g., version number, timestamp, nonce, rootinformation, difficulty level, consensus protocol, and/or otherparameters or information associated with the corresponding files and/orthe blockchain in general.

The files 774 ₂ to 774 _(N) in the other blocks may be equal to theoriginal file or may be a modified version of the original file in thegenesis block depending, for example, on the type of processingperformed. The type of processing performed may vary from block toblock. The processing may involve, for example, any modification of afile in a preceding block, such as redacting information or otherwisechanging the content of, taking information away from, or adding orappending information to the files.

Additionally, or alternatively, the processing may involve merelycopying the file from a preceding block, changing a storage location ofthe file, analyzing the file from one or more preceding blocks, movingthe file from one storage or memory location to another, or performingaction relative to the file of the blockchain and/or its associatedmetadata. Processing which involves analyzing a file may include, forexample, appending, including, or otherwise associating variousanalytics, statistics, or other information associated with the file.

The values in each of the other blocks 776 ₂ to 776 _(N) in the otherblocks are unique values and are all different as a result of theprocessing performed. For example, the value in any one blockcorresponds to an updated version of the value in the previous block.The update is reflected in the hash of the block to which the value isassigned. The values of the blocks therefore provide an indication ofwhat processing was performed in the blocks and also permit a tracingthrough the blockchain back to the original file. This tracking confirmsthe chain-of-custody of the file throughout the entire blockchain.

For example, consider the case where portions of the file in a previousblock are redacted, blocked out, or pixelated in order to protect theidentity of a person shown in the file. In this case, the blockincluding the redacted file will include metadata associated with theredacted file, e.g., how the redaction was performed, who performed theredaction, timestamps where the redaction(s) occurred, etc. The metadatamay be hashed to form the value. Because the metadata for the block isdifferent from the information that was hashed to form the value in theprevious block, the values are different from one another and may berecovered when decrypted.

In one embodiment, the value of a previous block may be updated (e.g., anew hash value computed) to form the value of a current block when anyone or more of the following occurs. The new hash value may be computedby hashing all or a portion of the information noted below, in thisexample embodiment.

-   -   a) new SHA-2 computed hash value if the file has been processed        in any way (e.g., if the file was redacted, copied, altered,        accessed, or some other action was taken)    -   b) new storage location for the file    -   c) new metadata identified associated with the file    -   d) transfer of access or control of the file from one blockchain        participant to another blockchain participant

FIG. 7D illustrates an embodiment of a block which may represent thestructure of the blocks in the blockchain 790 in accordance with oneembodiment. The block, Block_(i), includes a header 772 _(i), a file 774_(i), and a value 776 _(i).

The header 772 _(i) includes a hash value of a previous blockBlock_(i−1) and additional reference information, which, for example,may be any of the types of information (e.g., header informationincluding references, characteristics, parameters, etc.) discussedherein. All blocks reference the hash of a previous block except, ofcourse, the genesis block. The hash value of the previous block may bejust a hash of the header in the previous block or a hash of all or aportion of the information in the previous block, including the file andmetadata.

The file 774 _(i) includes a plurality of data, such as Data 1, Data 2,. . . , Data N in sequence. The data are tagged with metadata Metadata1, Metadata 2, . . . , Metadata N which describe the content and/orcharacteristics associated with the data. For example, the metadata foreach data may include information to indicate a timestamp for the data,process the data, keywords indicating the persons or other contentdepicted in the data, and/or other features that may be helpful toestablish the validity and content of the file as a whole, andparticularly its use a digital evidence, for example, as described inconnection with an embodiment discussed below. In addition to themetadata, each data may be tagged with reference REF₁, REF₂, . . . ,REF_(N) to a previous data to prevent tampering, gaps in the file, andsequential reference through the file.

Once the metadata is assigned to the data (e.g., through a smartcontract), the metadata cannot be altered without the hash changing,which can easily be identified for invalidation. The metadata, thus,creates a data log of information that may be accessed for use byparticipants in the blockchain.

The value 776 _(i) is a hash value or other value computed based on anyof the types of information previously discussed. For example, for anygiven block Block_(i), the value for that block may be updated toreflect the processing that was performed for that block, e.g., new hashvalue, new storage location, new metadata for the associated file,transfer of control or access, identifier, or other action orinformation to be added. Although the value in each block is shown to beseparate from the metadata for the data of the file and header, thevalue may be based, in part or whole, on this metadata in anotherembodiment.

Once the blockchain 770 is formed, at any point in time, the immutablechain-of-custody for the file may be obtained by querying the blockchainfor the transaction history of the values across the blocks. This query,or tracking procedure, may begin with decrypting the value of the blockthat is most currently included (e.g., the last (N^(th)) block), andthen continuing to decrypt the value of the other blocks until thegenesis block is reached and the original file is recovered. Thedecryption may involve decrypting the headers and files and associatedmetadata at each block, as well.

Decryption is performed based on the type of encryption that took placein each block. This may involve the use of private keys, public keys, ora public key-private key pair. For example, when asymmetric encryptionis used, blockchain participants or a processor in the network maygenerate a public key and private key pair using a predeterminedalgorithm. The public key and private key are associated with each otherthrough some mathematical relationship. The public key may bedistributed publicly to serve as an address to receive messages fromother users, e.g., an IP address or home address. The private key iskept secret and used to digitally sign messages sent to other blockchainparticipants. The signature is included in the message so that therecipient can verify using the public key of the sender. This way, therecipient can be sure that only the sender could have sent this message.

Generating a key pair may be analogous to creating an account on theblockchain, but without having to actually register anywhere. Also,every transaction that is executed on the blockchain is digitally signedby the sender using their private key. This signature ensures that onlythe owner of the account can track and process (if within the scope ofpermission determined by a smart contract) the file of the blockchain.

FIGS. 8A and 8B illustrate additional examples of use cases forblockchain which may be incorporated and used herein. In particular,FIG. 8A illustrates an example 800 of a blockchain 810 which storesmachine learning (artificial intelligence) data. Machine learning relieson vast quantities of historical data (or training data) to buildpredictive models for accurate prediction on new data. Machine learningsoftware (e.g., neural networks, etc.) can often sift through millionsof records to unearth non-intuitive patterns.

In the example of FIG. 8A, a host platform 820 builds and deploys amachine learning model for predictive monitoring of assets 830. Here,the host platform 820 may be a cloud platform, an industrial server, aweb server, a personal computer, a user device, and the like. Assets 830can be any type of asset (e.g., machine or equipment, etc.) such as anaircraft, locomotive, turbine, medical machinery and equipment, oil andgas equipment, boats, ships, vehicles, and the like. As another example,assets 830 may be non-tangible assets such as stocks, currency, digitalcoins, insurance, or the like.

The blockchain 810 can be used to significantly improve both a trainingprocess 802 of the machine learning model and a predictive process 804based on a trained machine learning model. For example, in 802, ratherthan requiring a data scientist/engineer or other user to collect thedata, historical data may be stored by the assets 830 themselves (orthrough an intermediary, not shown) on the blockchain 810. This cansignificantly reduce the collection time needed by the host platform 820when performing predictive model training. For example, using smartcontracts, data can be directly and reliably transferred straight fromits place of origin to the blockchain 810. By using the blockchain 810to ensure the security and ownership of the collected data, smartcontracts may directly send the data from the assets to the individualsthat use the data for building a machine learning model. This allows forsharing of data among the assets 830.

The collected data may be stored in the blockchain 810 based on aconsensus mechanism. The consensus mechanism pulls in (permissionednodes) to ensure that the data being recorded is verified and accurate.The data recorded is time-stamped, cryptographically signed, andimmutable. It is therefore auditable, transparent, and secure. AddingIoT devices which write directly to the blockchain can, in certain cases(i.e. supply chain, healthcare, logistics, etc.), increase both thefrequency and accuracy of the data being recorded.

Furthermore, training of the machine learning model on the collecteddata may take rounds of refinement and testing by the host platform 820.Each round may be based on additional data or data that was notpreviously considered to help expand the knowledge of the machinelearning model. In 802, the different training and testing steps (andthe data associated therewith) may be stored on the blockchain 810 bythe host platform 820. Each refinement of the machine learning model(e.g., changes in variables, weights, etc.) may be stored on theblockchain 810. This provides verifiable proof of how the model wastrained and what data was used to train the model. Furthermore, when thehost platform 820 has achieved a finally trained model, the resultingmodel may be stored on the blockchain 810.

After the model has been trained, it may be deployed to a liveenvironment where it can make predictions/decisions based on theexecution of the final trained machine learning model. For example, in804, the machine learning model may be used for condition-basedmaintenance (CBM) for an asset such as an aircraft, a wind turbine, ahealthcare machine, and the like. In this example, data fed back fromthe asset 830 may be input the machine learning model and used to makeevent predictions such as failure events, error codes, and the like.Determinations made by the execution of the machine learning model atthe host platform 820 may be stored on the blockchain 810 to provideauditable/verifiable proof. As one non-limiting example, the machinelearning model may predict a future breakdown/failure to a part of theasset 830 and create alert or a notification to replace the part. Thedata behind this decision may be stored by the host platform 820 on theblockchain 810. In one embodiment the features and/or the actionsdescribed and/or depicted herein can occur on or with respect to theblockchain 810.

New transactions for a blockchain can be gathered together into a newblock and added to an existing hash value. This is then encrypted tocreate a new hash for the new block. This is added to the next list oftransactions when they are encrypted, and so on. The result is a chainof blocks that each contain the hash values of all preceding blocks.Computers that store these blocks regularly compare their hash values toensure that they are all in agreement. Any computer that does not agree,discards the records that are causing the problem. This approach is goodfor ensuring tamper-resistance of the blockchain, but it is not perfect.

One way to game this system is for a dishonest user to change the listof transactions in their favor, but in a way that leaves the hashunchanged. This can be done by brute force, in other words by changing arecord, encrypting the result, and seeing whether the hash value is thesame. And if not, trying again and again and again until it finds a hashthat matches. The security of blockchains is based on the belief thatordinary computers can only perform this kind of brute force attack overtime scales that are entirely impractical, such as the age of theuniverse. By contrast, quantum computers are much faster (1000s of timesfaster) and consequently pose a much greater threat.

FIG. 8B illustrates an example 850 of a quantum-secure blockchain 852which implements quantum key distribution (QKD) to protect against aquantum computing attack. In this example, blockchain users can verifyeach other's identities using QKD. This sends information using quantumparticles such as photons, which cannot be copied by an eavesdropperwithout destroying them. In this way, a sender and a receiver throughthe blockchain can be sure of each other's identity.

In the example of FIG. 8B, four users are present 854, 856, 858, and860. Each of pair of users may share a secret key 862 (i.e., a QKD)between themselves. Since there are four nodes in this example, sixpairs of nodes exists, and therefore six different secret keys 862 areused including QKD_(AB), QKD_(AC), QKD_(AD), QKD_(BC), QKD_(BD), andQKD_(CD). Each pair can create a QKD by sending information usingquantum particles such as photons, which cannot be copied by aneavesdropper without destroying them. In this way, a pair of users canbe sure of each other's identity.

The operation of the blockchain 852 is based on two procedures (i)creation of transactions, and (ii) construction of blocks that aggregatethe new transactions. New transactions may be created similar to atraditional blockchain network. Each transaction may contain informationabout a sender, a receiver, a time of creation, an amount (or value) tobe transferred, a list of reference transactions that justifies thesender has funds for the operation, and the like. This transactionrecord is then sent to all other nodes where it is entered into a poolof unconfirmed transactions. Here, two parties (i.e., a pair of usersfrom among 854-860) authenticate the transaction by providing theirshared secret key 862 (QKD). This quantum signature can be attached toevery transaction making it exceedingly difficult to tamper with. Eachnode checks their entries with respect to a local copy of the blockchain852 to verify that each transaction has sufficient funds. However, thetransactions are not yet confirmed.

Rather than perform a traditional mining process on the blocks, theblocks may be created in a decentralized manner using a broadcastprotocol. At a predetermined period of time (e.g., seconds, minutes,hours, etc.) the network may apply the broadcast protocol to anyunconfirmed transaction thereby to achieve a Byzantine agreement(consensus) regarding a correct version of the transaction. For example,each node may possess a private value (transaction data of thatparticular node). In a first round, nodes transmit their private valuesto each other. In subsequent rounds, nodes communicate the informationthey received in the previous round from other nodes. Here, honest nodesare able to create a complete set of transactions within a new block.This new block can be added to the blockchain 852. In one embodiment thefeatures and/or the actions described and/or depicted herein can occuron or with respect to the blockchain 852.

FIG. 9 illustrates an example system 900 that supports one or more ofthe example embodiments described and/or depicted herein. The system 900comprises a computer system/server 902, which is operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system/server 902 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system/server 902 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 902 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 9 , computer system/server 902 in cloud computing node900 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 902 may include, but are notlimited to, one or more processors or processing units 904, a systemmemory 906, and a bus that couples various system components includingsystem memory 906 to processor 904.

The bus represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 902 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 902, and it includes both volatileand non-volatile media, removable and non-removable media. System memory906, in one embodiment, implements the flow diagrams of the otherfigures. The system memory 906 can include computer system readablemedia in the form of volatile memory, such as random-access memory (RAM)910 and/or cache memory 912. Computer system/server 902 may furtherinclude other removable/non-removable, volatile/non-volatile computersystem storage media. By way of example only, storage system 914 can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to thebus by one or more data media interfaces. As will be further depictedand described below, memory 906 may include at least one program producthaving a set (e.g., at least one) of program modules that are configuredto carry out the functions of various embodiments of the application.

Program/utility 916, having a set (at least one) of program modules 918,may be stored in memory 906 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 918 generally carry out the functionsand/or methodologies of various embodiments of the application asdescribed herein.

As will be appreciated by one skilled in the art, aspects of the presentapplication may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present application may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present application may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Computer system/server 902 may also communicate with one or moreexternal devices 920 such as a keyboard, a pointing device, a display922, etc.; one or more devices that enable a user to interact withcomputer system/server 902; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 902 to communicate withone or more other computing devices. Such communication can occur viaI/O interfaces 924. Still yet, computer system/server 902 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 926. As depicted, network adapter 926communicates with the other components of computer system/server 902 viaa bus. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 902. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Although an exemplary embodiment of at least one of a system, method,and non-transitory computer readable medium has been illustrated in theaccompanied drawings and described in the foregoing detaileddescription, it will be understood that the application is not limitedto the embodiments disclosed, but is capable of numerous rearrangements,modifications, and substitutions as set forth and defined by thefollowing claims. For example, the capabilities of the system of thevarious figures can be performed by one or more of the modules orcomponents described herein or in a distributed architecture and mayinclude a transmitter, receiver or pair of both. For example, all orpart of the functionality performed by the individual modules, may beperformed by one or more of these modules. Further, the functionalitydescribed herein may be performed at various times and in relation tovarious events, internal or external to the modules or components. Also,the information sent between various modules can be sent between themodules via at least one of: a data network, the Internet, a voicenetwork, an Internet Protocol network, a wireless device, a wired deviceand/or via plurality of protocols. Also, the messages sent or receivedby any of the modules may be sent or received directly and/or via one ormore of the other modules.

One skilled in the art will appreciate that a “system” could be embodiedas a personal computer, a server, a console, a personal digitalassistant (PDA), a cell phone, a tablet computing device, a smartphoneor any other suitable computing device, or combination of devices.Presenting the above-described functions as being performed by a“system” is not intended to limit the scope of the present applicationin any way but is intended to provide one example of many embodiments.Indeed, methods, systems and apparatuses disclosed herein may beimplemented in localized and distributed forms consistent with computingtechnology.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge-scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, comprise one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether but may comprise disparate instructions stored in differentlocations which, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, random access memory (RAM), tape, or any othersuch medium used to store data.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

It will be readily understood that the components of the application, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations.Thus, the detailed description of the embodiments is not intended tolimit the scope of the application as claimed but is merelyrepresentative of selected embodiments of the application.

One having ordinary skill in the art will readily understand that theabove may be practiced with steps in a different order, and/or withhardware elements in configurations that are different than those whichare disclosed. Therefore, although the application has been describedbased upon these preferred embodiments, it would be apparent to those ofskill in the art that certain modifications, variations, and alternativeconstructions would be apparent.

While preferred embodiments of the present application have beendescribed, it is to be understood that the embodiments described areillustrative only and the scope of the application is to be definedsolely by the appended claims when considered with a full range ofequivalents and modifications (e.g., protocols, hardware devices,software platforms etc.) thereto.

What is claimed is:
 1. A system, comprising: a training participantclient in a blockchain network, the training participant clientcomprising a processor that when executing one or more instructionsstored in a memory configures the training participant client to:generate, using a dataset, a gradient calculation corresponding to atraining iteration for machine learning model training comprising aplurality of training iterations, and generate a plurality oftransaction proposals that each correspond to a training iteration ofthe plurality of training iterations, each transaction proposalcomprising a corresponding gradient calculation, a batch comprising asubset of samples from the dataset, a loss function identifying a costmetric, and an original model parameter wherein the correspondinggradient calculation is stored in a blockchain of the blockchainnetwork; and an endorser node in the blockchain network, the endorsernode comprising a processor that when executing one or more instructionsstored in a memory configures endorser node to: receive the plurality oftransaction proposals from the training participant client, calculate afirst difference between the loss function at a first point of the twodifferent points and the loss function at a second point of the twodifferent points, calculate a second difference between the firstdifference and a product of a size of the batch, a random scalar, and adot product between the gradient calculation and a random direction,validate the corresponding gradient calculation based on the seconddifference being less than a predetermined threshold parameter, endorsea transaction proposal in response to the validation, and send theendorsed transaction proposal to the training participation client. 2.The system of claim 1, wherein the predetermined threshold parameter isapplication dependent.
 3. The system of claim 1, wherein the endorsernode is further configured to: generate the random scalar and the randomdirection; and obtain a new model parameter by perturbing the originalmodel parameter in the random direction with a step size equal to avalue of the random scalar.
 4. The system of claim 3, wherein, when theendorser node calculates the first difference, the endorser node isfurther configured to: evaluate a difference in the loss function of allthe samples in the batch between the new model parameter and theoriginal model parameter.
 5. The system of claim 1, wherein, in responseto a determination that the second difference is not less than thepredetermined threshold parameter, the endorser node is configured to:reject the corresponding transaction proposal.
 6. The system of claim 1,wherein, when the endorser node validates the corresponding gradientcalculation, the endorser node is further configured to: is furtherconfigured to: verify a correctness of the corresponding gradientcalculation based on parameters obtained from the correspondingtransaction proposal.
 7. A method, comprising: generating, by a trainingparticipant client using a dataset, a gradient calculation correspondingto a training iteration for machine learning model training comprising aplurality of training iterations, wherein the training participantclient is in a blockchain network; generating, by the trainingparticipant client, a plurality of transaction proposals that eachcorrespond to a training iteration of the plurality of trainingiterations, each transaction proposal comprising a correspondinggradient calculation, a batch comprising a subset of samples from thedataset, a loss function identifying a cost metric, and an originalmodel parameter wherein the corresponding gradient calculation is storedin a blockchain of the blockchain network; receiving, by an endorsernode in the blockchain network, the plurality of transaction proposalsfrom the training participant client, calculating a first differencebetween the loss function at a first point of the two different pointsand the loss function at a second point of the two different points;calculating a second difference between the first difference and aproduct of a size of the batch, a random scalar, and a dot productbetween the gradient calculation and a random direction; validating, bythe endorser node, the corresponding gradient calculation based on thesecond difference being less than a predetermined threshold parameter;endorsing, by the endorser node, a transaction proposal in response tothe validation; and sending, by the endorser node, the endorsedtransaction proposal to the training participation client.
 8. The methodof claim 7, wherein the predetermined threshold parameter is applicationdependent.
 9. The method of claim 7, further comprising generating therandom scalar and the random direction; and obtaining a new modelparameter by perturbing the original model parameter in the randomdirection with a step size equal to a value of the random scalar. 10.The method of claim 9, wherein the calculating the first differencefurther comprises: evaluating a difference in the loss function of allthe samples in the batch between the new model parameter and theoriginal model parameter.
 11. The method of claim 7, wherein, inresponse to determining that the second difference comparison is notless than the predetermined threshold parameter, the method furthercomprising: rejecting the corresponding transaction proposal.
 12. Themethod of claim 7, wherein the validating the corresponding gradientcalculation further comprises: verifying a correctness of thecorresponding gradient calculation based on parameters obtained from thecorresponding transaction proposal.
 13. A non-transitory computerreadable medium comprising one or more instructions that when executedby one or more processors associated with a training participant clientof a blockchain network and an endorser node of the blockchain networkcause the one or more processors to perform: generating, by the trainingparticipant client using a dataset, a gradient calculation correspondingto a training iteration for machine learning model training comprising aplurality of training iterations, wherein the training participantclient is in a blockchain network; generating, by the trainingparticipant client, a plurality of transaction proposals that eachcorrespond to a training iteration of the plurality of trainingiterations, each transaction proposal comprising a correspondinggradient calculation, a batch comprising a subset of samples from thedataset, a loss function identifying a cost metric, and an originalmodel parameter and wherein the corresponding gradient calculation isstored in a blockchain of the blockchain network; receiving, by anendorser node in the blockchain network, the plurality of transactionproposals from the training participant client, calculating a firstdifference between the loss function at a first point of the twodifferent points and the loss function at a second point of the twodifferent points; calculating a second difference between the firstdifference and a product of a size of the batch, a random scalar, and adot product between the gradient calculation and a random direction;validating, by the endorser node, the corresponding gradient calculationbased on the second difference being less than a predetermined thresholdparameter; endorsing, by the endorser node, a transaction proposal inresponse to the validation; and sending, by the endorser node, theendorsed transaction proposal to the training participation client. 14.The non-transitory computer readable medium of claim 13, wherein thepredetermined threshold parameter is application dependent.
 15. Thenon-transitory computer readable medium of claim 13, wherein the one ormore instructions further cause the processor to perform: generating therandom scalar and the random direction; and obtaining a new modelparameter by perturbing the original model parameter in the randomdirection with a step size equal to a value of the random scalar. 16.The non-transitory computer readable medium of claim 13, wherein, inresponse to determining that the second difference comparison is notless than the predetermined threshold parameter, the one or moreinstructions further cause the processor to perform: rejecting thecorresponding transaction proposal.
 17. The non-transitory computerreadable medium of claim 13, wherein the validating the correspondinggradient calculation further comprises: verifying a correctness of thecorresponding gradient calculation based on parameters obtained from thecorresponding transaction proposal.
 18. The non-transitory computerreadable medium of claim 13, wherein the calculating the firstdifference further comprises: evaluating a difference in the lossfunction of all the samples in the batch between the new model parameterand the original model parameter.